Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add robert-malai/anafpy --skill declaratie-preparegit clone --depth 1 https://github.com/robert-malai/anafpyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/robert-malai/anafpy/declaratie-prepare)<a href="https://agentmods.dev/skills/robert-malai/anafpy/declaratie-prepare"><img src="https://agentmods.dev/badge/skills/robert-malai/anafpy/declaratie-prepare/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/robert-malai/anafpy/declaratie-prepare"><img src="https://agentmods.dev/badge/skills/robert-malai/anafpy/declaratie-prepare.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00139 | $0.02328 |
| Opus 5 | $0.00069 | $0.01164 |
| Sonnet 5 | $0.00028 | $0.00466 |
| Haiku 4.5 | $0.00014 | $0.00233 |
Grade A, and why
declaratie-prepare scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build and file a tax declaration from source data
You are preparing a legal tax declaration for Romania's tax authority (ANAF) from whatever the user gives you — raw numbers, a document, or just a request. The flow is local until the filing step: identify the form → read its completion guide → infer what a lookup can answer → ask for everything else → author the XML → validate until ANAF's validator agrees → render → user review → sign → file on the portal (or hand off for manual filing) → confirm with the status tools.
Three rules override everything else:
- Never invent a value. Every amount, period, and election must trace to the source data, a lookup result, or the user's answer. When something is missing, ask — do not guess. ANAF's validator, not you, is the authority.
- Infer before you ask, ask before you assume. Identification data (company name, address, VAT status) comes from the CUI lookup, never from memory and never by asking the user to retype what ANAF publishes. Elections and amounts (settlement period, refund request, bank account, the numbers themselves) can never be inferred — always ask.
- Never self-approve the signature — or the filing.
declaratie_signanddeclaratie_portal_loginfire the user's certificate PIN/2FA, anddeclaratie_submitfiles a real declaration on the production portal (declarations have no test environment). Call each only after the user explicitly approved that step here, never chained on a single earlier "yes".
Step 1 — identify the form, orient the tooling
If the user already named the form (or it is unambiguous from their request),
do not ask again — state which form you are preparing ("Working on D300,
the VAT return for 06/2026") and move on. Otherwise infer it from the data and
situation using the inventory resource
anafref://declaratii/forms/README (173 forms, bucketed by SME usage) —
e.g. VAT numbers for a month → D300; per-partner domestic invoice lists →
D394; intra-EU lines → D390; payroll → D112; a prior-period correction of a
D100 → D710. Only when two forms remain plausible, present the candidates with
one line on who files each and ask.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 177 lines · 139 tokens per session scan A 484ad9928dc6
declaratie-prepare is a skill published in the GitHub repository robert-malai/anafpy (27 stars, last pushed 8d ago), licensed Apache-2.0. It adds 139 tokens to every session and 2,328 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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